{"id":"W4402031318","doi":"10.1021/acssensors.4c00806","title":"Class-Wide Analysis of Frizzled-Dishevelled Interactions Using BRET Biosensors Reveals Functional Differences among Receptor Paralogs","year":2024,"lang":"en","type":"article","venue":"ACS Sensors","topic":"Receptor Mechanisms and Signaling","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Innovative Medicines Initiative; Horizon 2020 Framework Programme; Novo Nordisk Fonden; Vetenskapsrådet; Cancerfonden; Karolinska Institutet; European Commission; Kungliga Tekniska Högskolan; Deutsche Forschungsgemeinschaft; Diamond Light Source; Novo Nordisk; European Federation of Pharmaceutical Industries and Associations; McGill University","keywords":"Dishevelled; Frizzled; Computational biology; Biology; Biosensor; Receptor; Class (philosophy); Evolutionary biology; Neuroscience; Cell biology; Genetics; Signal transduction; Computer science; Biochemistry; Wnt signaling pathway; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000215775,0.0002257321,0.0003692344,0.0003267833,0.0001120813,0.00006790202,0.000135385,0.0001869096,0.000989774],"category_scores_gemma":[0.0001358411,0.0001980187,0.0003732735,0.0005403992,0.0001059297,0.00001568347,0.0000663921,0.0001609621,0.00001915107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002916754,"about_ca_system_score_gemma":0.00004859929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009561227,"about_ca_topic_score_gemma":0.00006313072,"domain_scores_codex":[0.9984376,0.0001306296,0.0004484765,0.0004999806,0.0002213913,0.0002618741],"domain_scores_gemma":[0.9992298,0.0001217682,0.0001361316,0.0002872071,0.0001302966,0.00009483085],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004554527,0.00003368601,0.01917672,0.00002416208,0.001460777,0.000003528429,0.0001883873,0.002757861,0.9746622,0.0001734827,0.001343649,0.0001299805],"study_design_scores_gemma":[0.0003416209,0.000241332,0.03108927,0.0001753658,0.001645432,0.00001733119,0.001109362,0.02266849,0.9367571,0.0003384108,0.004886939,0.0007293026],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949149,0.0003275753,0.003457133,0.00008765999,0.0007042501,0.0001219591,0.00009388606,0.00003393965,0.0002586387],"genre_scores_gemma":[0.996478,0.0001208906,0.0008756907,0.00005861887,0.0002015819,0.000005771545,0.0002579951,0.00002910194,0.001972351],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03790509,"threshold_uncertainty_score":0.9999235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02475544220423765,"score_gpt":0.2658977820995616,"score_spread":0.2411423398953239,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}